Reinforcement Learning for Respondent-Driven Sampling
Abstract: Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of…
Abstract: Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of…
Abstract: Hierarchical Clustering (HC) is a widely studied problem in unsupervised learning and exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage…
Abstract: While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as censored covariates, is now of growing interest. So far,…
RSVP Today! Abstract: There has been a spike in concern about existential risk from artificial general intelligence, or AGI. This fear, commonly associated with terms…
Abstract: Methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion to solve planning problems. However,…
Abstract: Dr. Kaiser will discuss her past work applying AI-based techniques to software engineering problems and applying software engineering techniques to finding bugs in AI…
Abstract: Graphs and networks are widely used to represent complex systems such as genetic regulatory networks, brain connectivity networks, etc. Learning underlying graphs from high-dimensional…
Please join us on Wednesday, May 29, 2024, for the 4th Annual ICS Project Expo presented by the undergraduate capstone programs from Informatics, Computer Science, and Data Science,…
Check back for more information!
Check back for more information!